OpenAI's ChatGPT Agent: PC Control & Task Automation Explained

OpenAI has introduced ChatGPT Agent, a significant evolution of its flagship artificial intelligence model. This advanced iteration comes equipped with a virtual computing environment and an integrated toolkit, fundamentally transforming its capabilities. No longer limited to mere analysis or data retrieval, the agent can now execute complex, multi-step tasks by directly controlling a user’s computer. This leap in functionality, which still necessitates substantial human input and oversight, arrived amidst a period of rapid AI development, shortly before Meta researchers reported their AI models demonstrating signs of independent self-improvement and prior to OpenAI’s own release of GPT-5.

With ChatGPT Agent, users can instruct the large language model to not only process information but to act upon it. For instance, one could command the agent to review a calendar and compile a briefing on upcoming events, or to sift through a vast dataset and synthesize it into a concise summary or a presentation deck. While earlier large language models might provide recipes for a Japanese-style breakfast, ChatGPT Agent possesses the capacity to fully plan the meal and even purchase the necessary ingredients for a specified number of guests.

Despite its impressive new capabilities, the model is not without limitations. Like all current AI models, its spatial reasoning remains weak, hindering tasks that involve physical navigation or planning. Furthermore, it lacks true persistent memory, processing information in real-time without reliable recall or the ability to reference past interactions beyond immediate conversational context.

Nevertheless, ChatGPT Agent shows marked improvements in OpenAI’s internal benchmarking. On “Humanity’s Last Exam,” an AI benchmark evaluating a model’s ability to answer expert-level questions across various disciplines, the agent more than doubled the accuracy percentage of OpenAI o3 without tools, achieving 41.6% compared to 20.3%. It also significantly outperformed other OpenAI tools and a version of itself that lacked integrated tools like a browser and virtual computer. In the challenging “FrontierMath” benchmark, ChatGPT Agent, with its comprehensive toolkit, again surpassed previous models by a wide margin.

The agent’s architecture is built upon three foundational pillars derived from earlier OpenAI innovations. The first is ‘Operator,’ an agent designed to browse the web autonomously for users. The second is ‘deep research,’ developed to efficiently comb through and synthesize extensive datasets. The final component integrates previous versions of ChatGPT itself, renowned for their conversational fluency and presentation capabilities. According to Kofi Nyarko, a professor at Morgan State University and director of the Data Engineering and Predictive Analytics (DEPA) Research Lab, this integration means the agent “can autonomously browse the web, generate code, create files, and so on, all under human supervision.”

Nyarko, however, quickly emphasized that the new agent is far from fully autonomous. He cautioned that “hallucinations, user interface fragility, or misinterpretation can lead to errors. Built-in safeguards, like permission prompts and interruptibility, are essential but not sufficient to eliminate risk entirely.”

OpenAI itself has openly acknowledged the inherent dangers posed by the agent’s increased autonomy. Company representatives have stated that ChatGPT Agent possesses “high biological and chemical capabilities,” raising concerns that it could potentially assist in the creation of chemical or biological weapons. Biosecurity experts view AI agents like this as a “capability escalation pathway” compared to existing resources such as a chemistry lab and textbook. An AI can instantly draw upon countless resources, synthesize data across scientific disciplines, provide iterative troubleshooting akin to an expert mentor, navigate supplier websites, fill out order forms, and even help bypass basic verification checks.

With its virtual computer, the agent can also autonomously interact with files, websites, and online tools, amplifying the potential for harm if misused. The risk of data breaches, data manipulation, and misaligned behavior such as financial fraud is significantly heightened, particularly in the event of a prompt injection attack, where malicious commands are subtly embedded into user inputs, or other forms of hijacking. These risks, Nyarko noted, are in addition to those already implicit in traditional AI models and large language models, including the amplification of errors, the introduction of biases from public data, the complication of liability frameworks, and the unintentional fostering of psychological dependence.

In response to the new threats posed by a more agentic model, OpenAI engineers have also reinforced a number of safeguards. These measures include comprehensive threat modeling, dual-use refusal training—where the model is taught to reject harmful requests involving data that could have both beneficial and malicious applications—bug bounty programs, and expert “red-teaming” exercises focused on biodefense. Despite these efforts, a risk management assessment conducted in July 2025 by SaferAI, a safety-focused non-profit, evaluated OpenAI’s risk management policies as “Weak,” assigning them a score of 33% out of a possible 100%. Similarly, OpenAI received only a C grade on the AI Safety Index compiled by the Future of Life Institute, a prominent AI safety organization.